In complex systems, many kinds of data are relied upon to obtain results. Integrating and making sense of these disparate kinds of data is a challenging task. For example, in the context of Battle Management Command Control and Communications (BMC3) systems, position and identification of friendly, neutral, and enemy actors needs to be determined quickly based on a wide variety of possible data types. Data types to be utilized include infrared (IR), microwave, radar, electronic intelligence, human intelligence, unmanned aerial vehicle (UAV), network monitoring, application management, and procurement systems to name a few.
Systems have been developed to tackle these complex tasks. However, the software developed typically falls in one of 2 categories: Hard coded for scalability and performance, or data driven. Hard coded solutions cannot be reconfigured without development of a new release by programmers. On the other hand, data driven approaches quickly go out of control as the number of rules grows and exceptions become the rule. In both cases, the problem is that these systems are not adaptive—they utilize an architecture that is either predetermined or cumulative. In neither case is the architecture adaptive to changes in environment.
The typical architectures also break down information into data, losing critical context information in the process. Data driven solutions attempt to resolve this problem by storing business rules and other context information, but in systems where exceptions are the rule this approach can quickly get out of control.
Another problem is that such systems as have been deployed do not provide for real time solutions—the decision makers are made to wait for the information while the system slowly prepares it for presentation. In certain fast moving situations (e.g., battle, air traffic control), timeliness of the information is important to minimize risk of human life.
While technology evolves new ways of generating data, it takes a very long time to integrate these new data generating means into current decision system architectures. That is because the system must be re-programmed to accept something new or new rules must be developed following pre-ordained rule semantics from a type of sensor (or source of data) that was not envisioned when the most recent release of the system was made.
Thus, what is needed is a way to integrate and make sense of disparate kinds of information in a way that is adaptive to operational needs, provides real time solutions, and is adaptive to incorporating information received from newly developed technology.